Create README.md

README.md

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+---
+language:
+- ta
+metrics:
+- wer
+library_name: transformers
+pipeline_tag: automatic-speech-recognition
+---
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+This is the fine-tuned version of whisper-large-v2 model for Tamil language.
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <training_args = Seq2SeqTrainingArguments(
+    output_dir="./pretrainedwhisper-medium-native-v2",  # change to a repo name of your choice
+    per_device_train_batch_size=4,
+    gradient_accumulation_steps=1,  # increase by 2x for every 2x decrease in batch size
+    learning_rate=1e-5,
+    warmup_steps=200,
+    max_steps=2000,
+    gradient_checkpointing=True,
+    fp16=True,
+    evaluation_strategy="steps",
+    per_device_eval_batch_size=8,
+    predict_with_generate=True,
+    generation_max_length=225,
+    save_steps=500,
+    eval_steps=500,
+    logging_steps=25,
+    report_to=["tensorboard"],
+    load_best_model_at_end=True,
+    metric_for_best_model="wer",
+    greater_is_better=False,
+    push_to_hub=True,
+    optim="adamw_bnb_8bit"
+)>
+
+
+
+### Model Architecture and Objective
+
+The model follows the whisper architecture with the encoder-decoder part. Where the encoder used to create the embeddings from the speech input and the decoder used to give the textual outputs.
+
+
+